# Linear model with random effects: effects

## Load packages, read data and source custom scripts

```
rm(list = ls())
library(bamlss)
```

`#> Loading required package: coda`

`#> Loading required package: colorspace`

`#> Loading required package: mgcv`

`#> Loading required package: nlme`

`#> This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.`

```
#>
#> Attaching package: 'bamlss'
```

```
#> The following object is masked from 'package:mgcv':
#>
#> smooth.construct
```

`library(gamlss.dist)`

`#> Loading required package: MASS`

```
path_proj <- day2day::git_path()
path_data <- file.path(path_proj, "data")
path_processed <- file.path(path_data, "processed")
path_modelled <- file.path(path_data, "modelled")
source(file.path(path_proj, "src", "51-bamlss.R"))
bwdata_file <- file.path(path_processed, "bwdata_41_model.fst")
model_file <- file.path(path_modelled, "bw-muni-08-lm-re.rds")
form_file <- gsub("(\\.rds)$", "-form\\1", model_file)
model_file_burned <- gsub("(\\.rds)$", "-burned\\1", model_file)
bwdata_model <- fst::read_fst(bwdata_file)
form <- readRDS(form_file)
model <- readRDS(model_file_burned)
```

## Compute results

`model$results <- results.bamlss.default(model)`

## Summary

`summary(model)`

```
#>
#> Call:
#> bamlss(formula = form, data = bwdata_model, cores = 4, combine = FALSE,
#> light = TRUE, n.iter = 1000, burnin = 0)
#> ---
#> Family: gaussian
#> Link function: mu = identity, sigma = log
#> *---
#> Formula mu:
#> ---
#> born_weight ~ remoteness + prop_tap_toilet + s(res_muni, bs = "re")
#> -
#> Parametric coefficients:
#> Mean 2.5% 50% 97.5% parameters
#> (Intercept) 3200.15 3131.03 3203.72 3257.87 3232.79
#> remoteness -56.64 -124.94 -60.14 17.72 -57.49
#> prop_tap_toilet 149.47 31.29 142.96 293.85 -51.00
#> -
#> Acceptance probabilty:
#> Mean 2.5% 50% 97.5%
#> alpha 0.9920 0.9621 0.9988 1
#> -
#> Smooth terms:
#> Mean 2.5% 50% 97.5% parameters
#> s(res_muni).tau21 2142.83 1369.52 2067.18 3319.95 3823.12
#> s(res_muni).alpha 1.00 1.00 1.00 1.00 NA
#> s(res_muni).edf 41.81 41.25 41.84 42.25 42.35
#> ---
#> Formula sigma:
#> ---
#> sigma ~ 1
#> -
#> Parametric coefficients:
#> Mean 2.5% 50% 97.5% parameters
#> (Intercept) 6.225 6.223 6.225 6.228 6.225
#> -
#> Acceptance probabilty:
#> Mean 2.5% 50% 97.5%
#> alpha 0.9993 0.9937 1.0000 1
#> ---
#> Sampler summary:
#> -
#> runtime = 743.851
#> ---
#> Optimizer summary:
#> -
#> AICc = 4456381 edf = 46.3523 logLik = -2228144
#> logPost = -2228395 nobs = 291479 runtime = 9.681
```

## Parametric effects

```
par(mar = c(4, 4, 0.5, 0), mfrow = c(1, 2), cex.axis = 0.7)
plot2d_bamlss(model, bwdata_model, model = "mu", term = "remoteness", grid = 50, FUN = c95)
plot2d_bamlss(model, bwdata_model, model = "mu", term = "prop_tap_toilet", grid = 50, FUN = c95)
```

## Time to execute the task

Only useful when executed with `Rscript`

.

`proc.time()`

```
#> user system elapsed
#> 22.018 0.333 22.370
```